Objective: To explore the prospect of nonlinear autoregressive neural network in fitting and predicting the incidence tendency of hemorrhagic fever with renal syndrome (HFRS) , in the mainland of China.
Methods: Monthly reported case series of HFRS in China from 2004 to 2013 were used to build both ARIMA and NAR neural network models, in order to predict the monthly incidence of HFRS in China in 2014. Fitness and prediction on the effects of these two models were compared.
Results: For the Fitting dataset, MAE, RMSE and MAPE of the ARIMA model were 148.058, 272.077 and 12.678% respectively, while the MAE, RMSE and MAPE of NAR neural network appeared as 119.436, 186.671 and 11.778% respectively. For the Predicting dataset, MAE, RMSE and MAPE of the ARIMA model appeared as 189.088, 221.133 and 21.296%, while the MAE, RMSE and MAPE of the NAR neural network as 119.733, 151.329 and 11.431% respectively.
Conclusion: The NAR neural network showed better effects in fitting and predicting the incidence tendency of HFRS than using the traditional ARIMA model, in China. NAR neural network seemed to have strong application value in the prevention and control of HFRS.